| Dynamic Bayesian predictive synthesis in time series forecasting K McAlinn, M West Journal of Econometrics 210 (1), 155-169, 2019 | 120 | 2019 |
| Multivariate Bayesian predictive synthesis in macroeconomic forecasting K McAlinn, KA Aastveit, J Nakajima, M West Journal of the American Statistical Association 115 (531), 1092-1110, 2020 | 77 | 2020 |
| Dynamic variable selection with spike-and-slab process priors V Rockova, K McAlinn | 56 | 2021 |
| Divide and conquer: Financial ratios and industry returns predictability D Bianchi, K McAlinn Available at SSRN 3136368, 2020 | 13* | 2020 |
| Policy choice and best arm identification: Asymptotic analysis of exploration sampling K Ariu, M Kato, J Komiyama, K McAlinn, C Qin arXiv preprint arXiv:2109.08229, 2021 | 10 | 2021 |
| Dynamic sparse factor analysis K McAlinn, V Rockova, E Saha arXiv preprint arXiv:1812.04187, 2018 | 10 | 2018 |
| Mixed-frequency Bayesian predictive synthesis for economic nowcasting K McAlinn Journal of the Royal Statistical Society Series C: Applied Statistics 70 (5 …, 2021 | 9 | 2021 |
| Learning causal models from conditional moment restrictions by importance weighting M Kato, M Imaizumi, K McAlinn, H Kakehi, S Yasui arXiv preprint arXiv:2108.01312, 2021 | 8 | 2021 |
| The adaptive doubly robust estimator and a paradox concerning logging policy M Kato, K McAlinn, S Yasui Advances in Neural Information Processing Systems 34, 1351-1364, 2021 | 7 | 2021 |
| Bayesian Causal Synthesis for Supra-Inference on Heterogeneous Treatment Effects S Sugasawa, K Takanashi, K McAlinn arXiv preprint arXiv:2304.07726, 2023 | 5 | 2023 |
| Fully parallel particle learning for GPGPUs and other parallel devices K McAlinn, T Nakatsuma arXiv preprint arXiv:1212.1639, 2012 | 5 | 2012 |
| Volatility forecasts using stochastic volatility models with nonlinear leverage effects K McAlinn, A Ushio, T Nakatsuma Journal of Forecasting 39 (2), 143-154, 2020 | 4 | 2020 |
| Bayesian spatial predictive synthesis D Cabel, S Sugasawa, M Kato, K Takanashi, K McAlinn arXiv preprint arXiv:2203.05197, 2022 | 2 | 2022 |
| Learning causal relationships from conditional moment conditions by importance weighting M Kato, H Kakehi, K McAlinn, S Yasui arXiv preprint arXiv:2108.01312, 2021 | 2 | 2021 |
| Predictive properties and minimaxity of bayesian predictive synthesis K Takanashi, K McAlinn Preprint, RIKEN and Temple University, 2020 | 2 | 2020 |
| Predictions with dynamic Bayesian predictive synthesis are exact minimax K Takanashi, K McAlinn arXiv e-prints, arXiv: 1911.08662, 2019 | 2 | 2019 |
| Synthetic Control Methods by Density Matching under Implicit Endogeneitiy M Kato, A Ohda, M Imaizumi, K McAlinn arXiv preprint arXiv:2307.11127, 2023 | 1 | 2023 |
| Dynamic Mixed Frequency Synthesis for Economic Nowcasting K McAlinn arXiv preprint arXiv:1712.03646, 2017 | 1 | 2017 |
| Predicting the next executions using high-frequency data K Sugiura, T Nakatsuma, K McAlinn Data Analytics 2015, 95-100, 2015 | 1 | 2015 |
| Patent Waiver and Incentive to Innovate K McAlinn, AJ Naghavi, G Pignataro, S Sugasawa, K Yamada | | 2023 |
Mike WestDuke UniversityVerified email at nc.rr.com
